Global hypothesis test to simultaneously compare the predictive values of two binary diagnostic tests

Statistics in Transition New Series, 2015

Evaluating the effect of variables on diagnostic measures (sensitivity, specificity, positive, and negative predictive values) is often of interest to clinical researchers. Logistic regression (LR) models can be used to predict diagnostic measures of a screening test. A marginal model framework using generalized estimating equation (GEE) with logit/log link can be used to compare the diagnostic measures between two or more screening tests. These individual modeling approaches to each diagnostic measure ignore the dependency among these measures that might affect the association of covariates with each diagnostic measure. The diagnostic measures are computed using joint distribution of screening test result and reference test result which generates a multinomial response data. Thus, multinomial logistic regression (MLR) is a more appropriate approach to modeling these diagnostic measures. In this study, the validity of LR and GEE approaches as compared to MLR model was assessed for m...

Turkish Journal of Biochemistry, 2020

Objectives The aim of this study is to introduce the features of diagnostic tests. In addition, it will be demonstrated which performance measures can be used for diagnostic tests with binary results, the properties of these measures and how to interpret them. Materials and Methods The evaluation of the diagnostic test performance measures may differ depending on whether the test result is numerical or binary. When the diagnostic test result is continuous numerical data, ROC analysis is often utilized. The performance of a diagnostic test with binary results are usually evaluated using the measures of sensitivity and specificity. However, there are some important measures other than these two measures for binary test results. These measures are predictive values, overall accuracy, diagnostic odds ratio, Youden index, and likelihood ratios. Results A hypothetical data has been produced based on the studies conducted on the performance of rapid tests (Specific IgM/IgG) according to th...

Computational Statistics & Data Analysis, 2009

Calculating sample size to evaluate the accuracy of a binary diagnostic test and to compare the accuracy of two binary diagnostic tests is an important question in the study of diagnostic statistical methods. In the presence of partial disease verification, the disease status of some patients in the sample is unknown, so that the calculation of sample size can be complicated. A method to calculate sample size when evaluating the sensitivity and the specificity of a binary diagnostic test and when comparing the sensitivity and specificity of two binary tests in the presence of partial disease verification is proposed. The results obtained were applied to the diagnosis of coronary stenosis.

Journal of Biometrics & Biostatistics, 2012

Sensitivity and specificity are classic parameters to assess and to compare the precision of binary diagnostic tests in relation to a gold standard. Another parameter to assess and to compare the performance of binary diagnostic tests is the weighted kappa coefficient, which is a measure of the beyond-chance agreement between the binary diagnostic test and the gold standard, and it is a function of the sensitivity and the specificity of the diagnostic test, the disease prevalence and the relative loss between the false positives and the false negatives. In this study, we carry out a review of the weighted kappa coefficient, its estimation for a single diagnostic test and the hypothesis tests to compare the weighted kappa coefficients of two or more diagnostic tests, both when the gold standard is applied to all of the subjects in a random sample and when the gold standard is only applied to a subset of subjects in a random sample. The results were applied to different examples. Journa l o f B io m etrics & B io s ta tistics

BMC medical informatics and decision making, 2014

BackgroundUsing Monte Carlo simulations, we compare different methods (maximizing Youden index, maximizing mutual information, and logistic regression) for their ability to determine optimum binary cut-off thresholds for a ratio-scaled diagnostic test variable. Special attention is given to the stability and precision of the results in dependence on the distributional characteristics as well as the pre-test probabilities of the diagnostic categories in the test population.MethodsFictitious data sets of a ratio-scaled diagnostic test with different distributional characteristics are generated for 50, 100 and 200 fictitious ¿individuals¿ with systematic variation of pre-test probabilities of two diagnostic categories. For each data set, optimum binary cut-off limits are determined employing different methods. Based on these optimum cut-off thresholds, sensitivities and specificities are calculated for the respective data sets. Mean values and SD of these variables are computed for 100...

Journal of Data Science, 2009

Abstract: Comparison of more than two diagnostic or screening tests for prediction of presence vs. absence of a disease or condition can be com-plicated when attempting to simultaneously optimize a pair of competing criteria such as sensitivity and specificity. A technique for ...

Annual Review of Statistics and Its Application, 2021

In this review, we present an overview of the main aspects related to the statistical evaluation of medical tests for diagnosis and prognosis. Measures of diagnostic performance for binary tests, such as sensitivity, specificity, and predictive values, are introduced, and extensions to the case of continuous-outcome tests are detailed. Special focus is placed on the receiver operating characteristic (ROC) curve and its estimation, with emphasis on the topic of covariate adjustment. The extension to the case of time-dependent ROC curves for evaluating prognostic accuracy is also touched upon. We apply several of the approaches described to a data set derived from a study aimed to evaluate the ability of homeostasis model assessment of insulin resistance (HOMA-IR) levels to identify individuals at high cardio-metabolic risk and how such discriminatory ability might be influenced by age and gender. We also outline software available for the implementation of the methods.

Bonfring

The use of routine laboratory tests in diagnosing disease is becoming of increasing importance. This emphasizes to test the efficiency of diagnostic tests, since relatively few diagnostic tests correctly classify all subjects tested as diseased or well. The more usual situation is one in which some well subjects are classified as diseased and some diseased subjects classified as well. In this type of situation, Diagnostics and prognostic models serve the purpose. Diagnostic models are usually used for classification and quite commonly used in medical field. In this paper, importance of statistical classification procedures are highlighted which helps in the evaluation of diagnostic tests.

Journal of Applied Statistics, 2010

Statistics in Medicine, 2008

Non-inferiority is a reasonable approach to assessing the diagnostic accuracy of a new diagnostic test if it provides an easier administration or reduces the cost. The area under the receiver operating characteristic (ROC) curve is one of the common measures for the overall diagnostic accuracy. However, it may not differentiate the various shapes of the ROC curves with different diagnostic significances. The partial area under the ROC curve (PAUROC) may present an alternative that can provide additional and complimentary information for some diagnostic tests which require false-positive rate that does not exceed a certain level. Non-parametric and maximum likelihood methods can be used for the non-inferiority tests based on the difference in paired PAUROCs. However, their performance has not been investigated in finite samples. We propose to use the concept of generalized p-value to construct a non-inferiority test for diagnostic accuracy based on the difference in paired PAUROCs. Simulation results show that the proposed non-inferiority test not only adequately controls the size at the nominal level but also is uniformly more powerful than the non-parametric methods. The proposed method is illustrated with a numerical example using published data.